Proposal 1 Optimizing parallel iterative graph computation
نویسنده
چکیده
I propose to develop a deterministic parallel framework for performing iterative computation on a graph which schedules work on vertices based upon a valid coloring. Preliminary work modifying Graphlab, a parallel framework for implementing iterative machine learning algorithms on data graphs, has demonstrated the merits of this approach by improving performance and eliminating non-determinism. Further, I’ve identified opportunities to modify the graph representation to improve data locality as well as improvements and theoretical analysis of a deterministic parallel coloring algorithm used to pre-compute a coloring of the input graph.
منابع مشابه
Optimization of Agricultural BMPs Using a Parallel Computing Based Multi-Objective Optimization Algorithm
Beneficial Management Practices (BMPs) are important measures for reducing agricultural non-point source (NPS) pollution. However, selection of BMPs for placement in a watershed requires optimizing available resources to maximize possible water quality benefits. Due to its iterative nature, the optimization typically takes a long time to achieve the BMP trade-off results which is not desirable ...
متن کاملParallel computation framework for optimizing trailer routes in bulk transportation
We consider a rich tanker trailer routing problem with stochastic transit times for chemicals and liquid bulk orders. A typical route of the tanker trailer comprises of sourcing a cleaned and prepped trailer from a pre-wash location, pickup and delivery of chemical orders, cleaning the tanker trailer at a post-wash location after order delivery and prepping for the next order. Unlike traditiona...
متن کاملThesis Proposal Parallel Learning and Inference in Probabilistic Graphical Models
Probabilistic graphical models are one of the most influential and widely used techniques in machine learning. Powered by exponential gains in processor technology, graphical models have been successfully applied to a wide range of increasingly large and complex real-world problems. However, recent developments in computer architecture, large-scale computing, and data-storage have shifted the f...
متن کاملGraphTwist: Fast Iterative Graph Computation with Two-tier Optimizations
Large-scale real-world graphs are known to have highly skewed vertex degree distribution and highly skewed edge weight distribution. Existing vertex-centric iterative graph computation models suffer from a number of serious problems: (1) poor performance of parallel execution due to inherent workload imbalance at vertex level; (2) inefficient CPU resource utilization due to short execution time...
متن کاملDistributed-memory Parallel Algorithms for Distance-2 Coloring and Their Application to Derivative Computation∗
The distance-2 graph coloring problem aims at partitioning the vertex set of a graph into the fewest sets consisting of vertices pairwise at distance greater than two from each other. Its applications include derivative computation in numerical optimization and channel assignment in radio networks. We present efficient, distributed-memory, parallel heuristic algorithms for this NPhard problem a...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2012